{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import gym\n",
    "import itertools\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys\n",
    "\n",
    "if \"../\" not in sys.path:\n",
    "  sys.path.append(\"../\") \n",
    "\n",
    "from collections import defaultdict\n",
    "from lib.envs.windy_gridworld import WindyGridworldEnv\n",
    "from lib import plotting\n",
    "\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = WindyGridworldEnv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_epsilon_greedy_policy(Q, epsilon, nA):\n",
    "    \"\"\"\n",
    "    Creates an epsilon-greedy policy based on a given Q-function and epsilon.\n",
    "    \n",
    "    Args:\n",
    "        Q: A dictionary that maps from state -> action-values.\n",
    "            Each value is a numpy array of length nA (see below)\n",
    "        epsilon: The probability to select a random action . float between 0 and 1.\n",
    "        nA: Number of actions in the environment.\n",
    "    \n",
    "    Returns:\n",
    "        A function that takes the observation as an argument and returns\n",
    "        the probabilities for each action in the form of a numpy array of length nA.\n",
    "    \n",
    "    \"\"\"\n",
    "    def policy_fn(observation):\n",
    "        A = np.ones(nA, dtype=float) * epsilon / nA\n",
    "        best_action = np.argmax(Q[observation])\n",
    "        A[best_action] += (1.0 - epsilon)\n",
    "        return A\n",
    "    return policy_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sarsa(env, num_episodes, discount_factor=1.0, alpha=0.5, epsilon=0.1):\n",
    "    \"\"\"\n",
    "    SARSA algorithm: On-policy TD control. Finds the optimal epsilon-greedy policy.\n",
    "    \n",
    "    Args:\n",
    "        env: OpenAI environment.\n",
    "        num_episodes: Number of episodes to run for.\n",
    "        discount_factor: Gamma discount factor.\n",
    "        alpha: TD learning rate.\n",
    "        epsilon: Chance the sample a random action. Float betwen 0 and 1.\n",
    "    \n",
    "    Returns:\n",
    "        A tuple (Q, stats).\n",
    "        Q is the optimal action-value function, a dictionary mapping state -> action values.\n",
    "        stats is an EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Number of actions\n",
    "    nA = env.action_space.n\n",
    "    \n",
    "    # The final action-value function.\n",
    "    # A nested dictionary that maps state -> (action -> action-value).\n",
    "    Q = defaultdict(lambda: np.zeros(env.action_space.n))\n",
    "    \n",
    "    # Keeps track of useful statistics\n",
    "    stats = plotting.EpisodeStats(\n",
    "        episode_lengths=np.zeros(num_episodes),\n",
    "        episode_rewards=np.zeros(num_episodes))\n",
    "\n",
    "    # The policy we're following\n",
    "    policy = make_epsilon_greedy_policy(Q, epsilon, env.action_space.n)\n",
    "    \n",
    "\n",
    "    for i_episode in range(num_episodes):\n",
    "        \n",
    "        # GLIE criteria\n",
    "        epsilon /= (i_episode + 1)\n",
    "        \n",
    "        # Print out which episode we're on, useful for debugging.\n",
    "        if (i_episode + 1) % 100 == 0:\n",
    "            print(\"\\rEpisode {}/{}.\".format(i_episode + 1, num_episodes))\n",
    "            sys.stdout.flush()\n",
    "        \n",
    "        state = env.reset()\n",
    "        terminated = False\n",
    "        \n",
    "        for t in itertools.count():\n",
    "            \n",
    "            # sample action following epsilon greedy policy\n",
    "            action = np.random.choice(nA, p=policy(state))\n",
    "            \n",
    "            # Perform the action -> Get the reward and observe the next state \n",
    "            new_state, reward, terminated, _ = env.step(action)\n",
    "            \n",
    "            # Choose the action for the next state following our current policy\n",
    "            next_action = np.random.choice(nA, p=policy(new_state))\n",
    "            \n",
    "            stats.episode_rewards[i_episode] += reward\n",
    "            stats.episode_lengths[i_episode] = t\n",
    "\n",
    "            # value that we should have got\n",
    "            td_target = reward + discount_factor * Q[new_state][next_action]\n",
    "            td_error = td_target - Q[state][action]\n",
    "            \n",
    "            # SARSA update\n",
    "            Q[state][action] += alpha * td_error\n",
    "            \n",
    "            # Update current state\n",
    "            state = new_state\n",
    "            \n",
    "            if terminated:\n",
    "                break\n",
    "    \n",
    "    return Q, stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 100/200.\n",
      "Episode 200/200.\n"
     ]
    }
   ],
   "source": [
    "Q, stats = sarsa(env, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b60468550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b2c383d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QIGmtnQvc6JwbBpzODwc5HAT80VpbDQTARc65egdKiIiISP7zq1YSPHwnjHsf+vYnccFlmLbt4w4rLxjvfdwxZIqfN29erAGo2zykPCgHKcqDcgDKQYryUH8O/JwZBHfdDEuXYE48G3PkSZhE016vIO3SqtnUc8V9aVVEREQKlC+dTXDbb6G4GYmrbsH07L3+F8n3qJATERGRrAs+ehf/0L+gefNwepHOXeMOKS+pkBMREZGsCl55Cv/kg9CzN4kLr8J06BR3SHlLhZyIiIhkTfDCCPyzj2L2OhBz/qWY4mZxh5TXVMiJiIhIo/NBLSuG3xUWcfscijnvEkyiKO6w8p4KOREREWlUfsVygqF/ZfWXn2EOPgpz5oUq4jJEhZyIiIg0Gl9bS/CfW2D6V7S9+DpW9dsn7pCalKY9UYuIiIjExi+aT/D36+HrLzDnXkyLgcfGHVKTox45ERERyShfU41//Xn88yMgkcCcdymJfQ6NO6wmSYWciIiIZIwvnRVeSl1QCv0GkDjzQk0v0ohUyImIiEhG+DkzCG67AYqakbjkd5hd9ow7pCZPhZyIiIhsMj9rGsHtvwtXarjiT5gttFJDNmiwg4iIiGwSP2NK2BO3WQsSv/mzirgsUo+ciIiIbLTgw9H4//4bWrUJ10ztuEXcIRUUFXIiIiLSYL7yW/x//40f85bWTI2RCjkRERFpEL9oPsG//wzzZmOOOwPzY4sp0koNcVAhJyIiIhvMfz6O4L5bAUPikhsxO+8ed0gFTYWciIiIrJevqsQ/NRz/+vPQfVsSv7wW02nLuMMqeCrkREREZJ38rKkEw26H+XMwh/4Yc8ogTPPmcYclqJATERGRdQjefBH/+H3Qph2Jy/6A6btb3CFJGhVyIiIiUq/gnVfxj94TLrV13qWYVq3jDknqUCEnIiIiP+BnTME/8h/YaTcSF12NKW4Wd0hSj6wUctba+4FjgUXOuZ2jtt8DPwcWR4dd55x7Kdp3LXABUAtc4px7JRtxioiIFDrvPf6dV/Hufti8HYmf/0ZFXA7LVo/cg8CdwPA67bc7525Nb7DW9gVOB3YCugL/s9bu4JyrzUagIiIihcrX1OAfvAM/9m3YcVcSgy7BtGoTd1iyDllZa9U5Nxoo38DDTwBGOOcqnXMzgKnAgEYLTkRERPAVSwjuvAk/9m3MCWeRuOyPWm4rD8R9j9zF1tqfAh8DVzjnKoBuwJi0Y+ZGbSIiItIIgndexbthUFuLOfuXJA4+Ku6QZAPFWcjdDdwE+Ojn34HzG3ICa+1gYDCAc45kMpnpGBukuLg49hhygfKgHKQoD8oBKAcpuZgHX1PDyuF3sfr5xynZZQ/a/OJqirt0b7T3y8UcxKG4OHPlV2yFnHNuYeq5tfZe4IVosxTYKu3Q7lFbfecYCgyNNn1ZWVkjRLrhkskkcceQC5QH5SBFeVAOQDlIybU8+C/GEbj7w0l+Bx5HjT2fpYkiaMQYcy0HcUkmk5SUlGTkXLEVctbaLs65+dHmScAX0fPngEettbcRDnboBXwYQ4giIiJNjl80n8ANg08/hC26kBhyPab/3nGHJRspW9OPPAYcAiSttXOBG4FDrLX9CS+tzgQuBHDOTbTWOmASUAMM0YhVERGRTeMrK/GjnsSPegqKijCnnIsZeDymmaYWyWdZKeScc2fU0zxsHcffDNzceBGJiIgUDv/V5wQP3AFLFmEGHIw5dRCmfce4w5IMiHvUqoiIiDQi/9UXBP/8A3ToROLKP2N22DnukCSDVMiJiIg0UeFaqf+BZGcSV/4fpm27uEOSDFMhJyIi0sT46mr8iHvxo0dBn34kBl+Jad027rCkEaiQExERaUL87OkED98FM6dgjjoFc9LZmERR3GFJI1EhJyIi0gT4mhr80w/jX3sWWrchcdE1mD32izssaWQq5ERERPKcX7WC4O5b4KvPMQf9CHPyuZhWreMOS7JAhZyIiEge8yuXE9z223CFhvMvI7HvoXGHJFmkQk5ERCQP+eoq/OhX8C8/CatWkhhyA2bn3eMOS7JMhZyIiEge8UEt/oO38M8+AhVlsMPOJE4dhNl2h7hDkxiokBMREckTfuJ4gicfgLkzoUcvEuf9GnbcFWNM3KFJTFTIiYiI5DhfOovA3Q+TxkOyM+bnv8HseQAmkYg7NImZCjkREZEc5b/9Bv/8CPz/noXNWmLsBZhDjtFC9/IdFXIiIiI5xnsP48cQPH4vlJdhDjwSc/JPtTqD/IAKORERkRziVywjGH4XTBgD3XuQ+PmVmO37xB2W5CgVciIiIjnCfz6O4ME7YPVKzKmDMIefgCnS8lqydirkREREYuYrK/EjH8C/+RJ024bEZX/AdN827rAkD6iQExERiZGfNZXgvttgwVzMESdgTjoH06wk7rAkT6iQExERiYFfvRL/3GP4N1+Etu1JXH4Tpk+/uMOSPKNCTkREJMv8px8RDP8XrFiGOfgozIlnY1q1iTssyUMq5ERERLLEV36Lf+J+/NujwhGpl9yI2Wa7uMOSPKZCTkREJAuqZ3xN8JfrYWEp5siTwl44Tewrm0iFnIiISCPzn35I+b23QotWuhdOMiorhZy19n7gWGCRc27nqO1vwHFAFTANOM85t9Ra2wOYDHwVvXyMc+6ibMQpIiKSacHrL+Afv4/injsQXHQNpl2HuEOSJiRbPXIPAncCw9PaXgOudc7VWGv/AlwLXB3tm+ac65+l2ERERDLOL5hL8NRwGD8G+u9Nh6v/zJKVq+IOS5qYrBRyzrnRUU9beturaZtjgFOzEYuIiEhj8lWV+KeGh9OKNGse3gt39CmYzVqACjnJsFy5R+584PG07W2tteOB5cANzrl34glLRERkw/nSWQT3/BXmzwmnFTn+TEzbdnGHJU1Y7IWctfZ6oAZ4JGqaD2ztnFtird0DeMZau5Nzbnk9rx0MDAZwzpFMJrMVdr2Ki4tjjyEXKA/KQYryoBxA4eSgauJ4lv71WhLNm9P2xn/QvP+A7+0vlDysi3IQKi7OXPllvPcZO9m6RJdWX0gNdojaBgEXAgOdc6vX8rq3gN845z5ez1v4efPmZSbYjZRMJikrK4s1hlygPCgHKcqDcgBNPwe+pgb/0hP4Fx+HLbqSuPQPmI6dfnBcU8/DhlAOQslkkpKSEgCzqeeKrUfOWnsUcBVwcHoRZ63tBJQ752qttT2BXsD0mMIUERFZKz9/LsH9t8PMKZi9D8accSGmVeu4w5ICkq3pRx4DDgGS1tq5wI2Eo1SbA69Za2HNNCMHAX+01lYDAXCRc648G3GKiIhsqOC9/+Ef+Q+UNCdx4VWYPQ+IOyQpQNkatXpGPc3D1nLsSGBk40YkIiKycXxlJf6ph/BvvAB9+pE4/zLNDSexiX2wg4iISD7w3sOEsQQj7oXyxZiBx2F+cj6mqCju0KSAqZATERFZD79oPsFjQ+GLcdBtGxJX/hmzw87rf6FII1MhJyIisg7B+6/j/3s3FBVh7AWYQ3+MyeD0ESKbQt9EERGRevggwD9xP/5/z0HvXUj87HJMu45xhyXyPSrkRERE6vArlhE8cjeMex9z2LFhT5zuhZMcpEJOREQk4levwr89Cv/yE1D5LebU8zBHnogxmzxvq0ijUCEnIiIFzQcBfPU5/t3/4cd/ANVV0G8AiVPOxXTZKu7wRNapQYWctbYjcAzQxTn3V2ttVyDhnJvbKNGJiIg0Il9TTXDvrfDJB9CyFWb/gZj9D8f06BV3aCIbZIMLOWvtwYQT9X4M7A/8lXD5rN8AxzVKdCIiIo3E11QTDP0bjB+DOflczOHHYZqVxB2WSIMkGnDsP4DTnHNHATVR21hgQMajEhERaUR+WQXB3beERdwZg0kcfYqKOMlLDbm02sM593r03Ec/qxp4DhERkdj42lr8qJH4l0dCTRXmzItIHHpM3GGJbLSG9MhNstb+qE7b4cDnGYxHRESkUXjv8Q/fiX/mv9C3H4k/3KUiTvJeQ3rTrgBesNa+CLSw1t5DeG/cCY0SmYiISIb46V8RjHwQvp6IOe50EsefGXdIIhmxwYWcc26MtbYfcBZwPzAHGKARqyIikov8N6vx4z/Aj30bJk2ANptjzvoF5uCj4g5NJGMadH+bc66UcLSqiIhITvKL5uGf/i9+wlioqYZkZ8wJZ4WjUjdrGXd4Ihm1zkLOWvswawY2rJVz7qcZi0hERGQj+Npa/GvP4J97DIqLMQcfhRlwEGy7g1ZmkCZrfT1yU9OeJ4FzgeeBWcDWhPfIPdQ4oYmIiGwYP3s6wUP/gtnTYLd9SJx5oRa4l4KwzkLOOfeH1HNr7SvAj51z76S1HQD8tvHCExERWbdg9Cv4R++BVq1JXHQNZo/94g5JJGsaco/cPsCYOm1jgX0zF46IiMiG8YsX4J9+GP/RO9B3NxKDf4Np1SbusESyqiHzyI0H/mytbQEQ/bwZmNAYgYmIiNTHf7ua4In7CX77S/ynYzHHnk7ikt+piJOC1JAeuUHAo8Aya20F0J5w3VVNxiMiIlnhJ4wleORuWFYRLm5//JmY9roXTgpXQ+aRmwnsZ63dCugKzHfOzW6swERERNIFb72Ef+Q/sNW2JH5xLaZn77hDEoldg+aRs9a2Bw4FugGl1trnnXMVG/ja+4FjgUXOuZ2jtg7A40APYCZgnXMV1loD3AEcA6wGBjnnPmlIrCIi0jT4OTPCVRkmjod+A0hceJUWuBeJbPA9ctbafYFpwEXArsCFwLSofUM8CNSdTvsa4HXnXC/g9Wgb4GigV/QYDNy9oXGKiEjT4FevJHj43wQ3XQozpmBOPS8claoiTuQ7DemR+wfwS+fciFSDtfY04J/AXut7sXNutLW2R53mE4BDoucPAW8BV0ftw51zHhhjrW1nre3inJvfgHhFRCQP+eoq/Ptv4J9/DJYvwww8HnPsaZhWreMOTSTnNKSQ2wFwddqeBP6zCe/fOa04WwB0jp53I1zLNWVu1KZCTkSkifI1Nfg3X8S/8jQsK4dtdyDxq99ittk+7tBEclZDCrkpwOmEI1dTfkJ4uXWTOee8tXa9y4Gls9YOJrz0inOOZDKZiVA2WnFxcewx5ALlQTlIUR6UA9iwHFR9+Tkr7v4LNbOnU7LLHrQ89feU7LJHk1paS98F5SCluLhBQxTWfa4GHHsp8IK19hLCJbp6EN7DduwmvP/C1CVTa20XYFHUXgpslXZc96jte5xzQ4Gh0aYvKyvbhFA2XTKZJO4YcoHyoBykKA/KAaw9B957mPwpwaiRMPlTaJ8kMeR6avvvzQqAJUuyHmtj0ndBOUhJJpOUlGTmXs+GTD/yvrV2O+DHhNOPPA+85Jwr34T3f45w/dZbop/PprVfbK0dAewNLNP9cSIiTYdfsZzgvr/DpPGweXvMyediDj0as1nLuEMTySsN6tuLphr5L4C1tifQFtigQs5a+xjhwIaktXYucCNhAeestRcQ9vLZ6PCXCKcemUo4/ch5DYlTRERyk/cexo8hcMPCSX1P/znmoKMwzZrFHZpIXtrgQi4qxP4V9cydB/wbCKy1lzjnhq3v9c65M9aya2A9x3pgyIbGJiIiuc/PnELw6D0w42vYshuJq27BbNsr7rBE8lpDeuQGEl7+BLgcOBxYCjwDrLeQExGRwuUnf0pw183QohXm3F9h9j0MU1QUd1giea8hhVyJc67KWtsN6OCcew/AWtt5Pa8TEZEC5SuWsOzR/xC89TJ03ZrEpX/AtOsQd1giTUZDCrkJ1tprgW2AFwGiom55YwQmIiL5zY97n+CBf/BtbS3myBMxx1hMy1ZxhyXSpDSkkLsAuAmoBq6M2vYFHsl0UCIikp98VSVMGo//+D382Ldhux1JXvknKoq0rJZIY2jI9CPTgDPrtD1JuLqDiIgUKB/UwmcfEXzwJnzxCVRVQstWmIHHYU4ZRFHnLqC5w0QaxToLOWvtOc65h6Pn56/tOOfc/ZkOTEREcpuv/Bb/9ij8my9C2cJwPrj9DsPstg/ssAsmg7PXi0j91vdbdgbwcPT8nLUc4wEVciIiBcTPmUEw9G+wYC706kvi1EHQfx+NRBXJsnUWcs65Y9KeH9r44YiISC7zNdX4USPxLzpo1ZbEZX+MpAAoAAAfk0lEQVTE9O0fd1giBatB/d7W2nasWaJrHvCic25pYwQmIiK5xS9eEM4FVzoLs9eBmDMGY9psHndYIgUtsaEHWmsPA2YClwB7Ab8CZlprf7Ayg4iINC1+6iSCW66CpeUkfvVbEoOvVBEnkgMa0iN3JzDYOedSDdbanwB3ATtmOjAREYmfX70S//R/8W+/DB06kfjNjZguW8UdlohEGlLIdQVG1ml7Grg3c+GIiEgu8DU14YjUFx6DVavCqUROOAuzWYu4QxORNA0p5B4mXMj+n2ltvwCGZzQiERGJlZ8yieDhu2D+HNhxVxI/OR+zdc+4wxKRejSkkNsNuMhaexVQCnQDtgDGWmtHpw5yzh2U2RBFRCQb/KqV+JEP4t95NbyMOuR66DcAY0zcoYnIWjSkkLsXXUYVEWlyvPf4D0fjH78PVq3AHHkS5rjTdRlVJA+st5Cz1v7TOXeJc+6haPsC59ywtP0jnXOnNGaQIiLSOPyCUoIRQ2HieOjRi8Slf9BlVJE8siE9coMIpxxJ+RswLG37iEwGJCIijc8vKMW/5PBj3obmzcM54Q45GpPQygwi+WRDCrm6N0esb1tERHKQXzgP/8kH+PEfwIyvoaQEc8TxmB+dhGnbPu7wRGQjbEgh5xu4LSIiOcJ/+w1+7NvhPHBzZoSNPXphTv4pZv+BKuBE8tyGFHLF1tpDWdPzVndb/fAiIjnGL5iLf+tl/PuvwzerYattMaf9DLPbvpiOneIOT0QyZEMKuUXA/WnbS+psL8poRCIistH85+MIXnsGJn8KRcWYPfbHHHoMbLejphERaYLWW8g553pkIQ4REdkEvmIJwWP3wPgx0D6JOfFszIFH6NKpSBPXkHnkREQkB/lP3id46F9QXY05+VzMESdgivXnXaQQxPqbbq3tDTye1tQT+B3QDvg5sDhqv84591KWwxMRyWm+qhLvhuHfHhXOAffzKzBbdI07LBHJolgLOefcV0B/AGttEeHSX08D5wG3O+dujTE8EZGc5RfNI/j3/0HprHD6kBPPxhQ3izssEcmyRNwBpBkITHPOzYo7EBGRXOUrK/Hj3iP4vythWTmJX/+exKnnqYgTKVC5dBPF6cBjadsXW2t/CnwMXOGcq6j7AmvtYGAwgHOOZDKZlUDXpri4OPYYcoHyoBykKA+ZyUFteRmVH46m8uP3qPp8HFRVUdR1a9rdcCvFXbpnKNLGo+9BSHlQDlKKM3gPq/E+/vl8rbUlwDxgJ+fcQmttZ6CMcLLhm4Auzrnz13MaP2/evEaOdN2SySRlZWWxxpALlAflIEV52LQceO/xb7+Mf+IBqKqETltidt0Ls+tesMNOedMLp+9BSHlQDlKSySQlJSWQgdWxcqVH7mjgE+fcQoDUTwBr7b3AC3EFJiISB790CcGD/wwXs++7G4nTLoAuW2kuOBH5nlwp5M4g7bKqtbaLc25+tHkS8EUsUYmIZJmv/Bb/7mv45x6DmirMmReFi9mrgBOResReyFlrWwFHABemNf/VWtuf8NLqzDr7RESaHF9TjX95JP6N52HlCthhJxLnDMFsmfv3wIlIfGIv5Jxzq4COddrOiSkcEZGs899+Q3D3LTBpPPQbQOKokzHb9407LBHJA7EXciIihczX1BDc/X/w5WeYc39F4oAj4g5JRPKICjkRkZj4mVMInnwQvvocM+gSEvsfHndIIpJnVMiJiGSZX7II/8QD+HHvQes2mLN/qSJORDaKCjkRkSzxQYB/+Un8Sw4Ac+zpmCNPxLRoGXNkIpKvVMiJiGSB/2Y1wYN3wCcfwB77kfjJBZiOneIOS0TynAo5EZFG4oOAqi8+IXhxJP6T96C6GnPaz0gcfnzcoYlIE6FCTkQkQ/zScpg5BR89mDWVipUroEVLzD6HYQ46ErPN9nGHKSJNiAo5EZFN4Kur8U8Px3/0LixdEjYmEtB1a0z/fWiz1/6s3G4nTPPm8QYqIk2SCjkRkY3kF8wluO82mDUVdt8P06sPpkcv2Gq77wq3Fskkq7RIuIg0EhVyIiIN5Guq8aNG4l90ULIZiSHXY/rvHXdYIlKAVMiJiDSAnzqJYPhdMH8OZq8DMaf9DLN5+7jDEpECpUJORGQ9fHUV/pMP8O++Bl9+Bh23IHHJ7zC77Bl3aCJS4FTIiYishQ9q8a8+g3/5SVi9CpKdMSeejRl4HGazFnGHJyKiQk5EpD5+QWk4ge+0L2HXvcK533rvgkkk4g5NROQ7KuRERNL4oBb/5kv4px6C4maYn12BGXAQxpi4QxMR+QEVciIiEf/VFwQj7oW5M2DnPUicezGmXce4wxIRWSsVciJS8Lz3+GcfCacT6dAJM/hKzJ4HqBdORHKeCjkRKWj+22/wj/wHP+ZNzP6HY868EFOiVRhEJD+okBORguVnTycY+jdYNA9z/JmYY09TL5yI5BUVciJScHx1Ff6tl8MBDa3bkrjiT5jeu8QdlohIg6mQE5GC4ReU4kePwr//BqxaEU4rMujXmDZt4w5NRGSjqJATkSbP11Tjn3sUP+opSCSg/94kDjoK+vTTpVQRyWs5UchZa2cCK4BaoMY5t6e1tgPwONADmAlY51xFXDGKSH7yX08keGwozJ2BOfBIzAlnaW1UEWkycqKQixzqnCtL274GeN05d4u19ppo++p4QhORfOOXluMfvw//8bvQPkliyHWY/vvEHZaISEblUiFX1wnAIdHzh4C3UCEnIuvhv1mN/3B0OJChqgpz3OmYH52Caa4pRUSk6cmVQs4Dr1prPXCPc24o0Nk5Nz/avwDoXPdF1trBwGAA5xzJZDJb8daruLg49hhygfKgHKRkKw/B6lVUfjiayvffpHLCh1BdRbM+/Wg75BqKu23T6O+/LvouKAcpyoNykFJcnLnyy3jvM3ayjWWt7eacK7XWbgG8BvwKeM451y7tmArn3LpubPHz5s1r7FDXKZlMUlZWtv4DmzjlQTlIaew8+CDAv/86fuSDsHIFtE9i9tgPs/t+sN2OObHAvb4LykGK8qAcpCSTSUpKSgA2ebRVTvTIOedKo5+LrLVPAwOAhdbaLs65+dbaLsCiWIMUkZziyxYSPPAP+HoibN+XxCk/hZ65UbyJiGRL7IWctbYVkHDOrYieHwn8EXgOOBe4Jfr5bHxRikiu8LW1+HdfC3vhvMf89OJwaS0VcCJSgGIv5AjvfXvaWgthPI8650ZZaz8CnLX2AmAWYGOMUURygJ/8KcGIe2HebOi9C4lBl2CSP7h9VkSkYMReyDnnpgP96mlfAgzMfkQikmt8EOBfcvjnHoNOW5L4xbWw2z6azFdECl7shZyIyLr4uTMI/ns3TPsSs88hmLOHaCoREZGICjkRyQk+CKBsIcybjZ83e83P0lnQsjVm0K8x+x2mXjgRkTQq5EQkNuEl0yfw48fAgjlQVbVmZ4ckdN0as+temMOPx7TWwvYiInWpkBORWPjlFQSP3AOfvA877IQ56GjouhWm69ZhAdeiZdwhiojkPBVyIpI1vroK5s/Bvz0K//4bUFuLsReEPW66ZCoi0mAq5EQko3wQQMUSWFjK6pXLCKZ9hV9YCgtKoXwxeA/FzTD7DcQceSKmc9e4QxYRyVsq5EQkI3xtLf6tl/HPPQqrVwKwAqB5C9iyG2a7PrDfQOjcFdNnV0zbda24JyIiG0KFnIhsMj/9K4JH7obZ06Fv/3C9087d6NBnF8prvS6biog0EhVyIrLRfOW3+Cfux49+BTZvT+LCq2CP/b8r3Io6JDFaIFtEpNGokBORBvPeQ+lMgvtug3lzMAOPwxx/pkaaiohkmQo5EdkgftUKmPwpftIE/KQJsGQRtGpD4tIbMX13izs8EZGCpEJOROrll1XAlIn4KZPwX0+E0pnhiNMWLWHHXTFHnYzZbV/M5hq0ICISFxVyIgXOew/Ll8KcGfi5M2DOTPzMKbBoXnhASXPYbkfMcWdg+vaHHr0wRUXxBi0iIoAKOZGC5Etn4T94Az97OsydCSuWrdnZIQlb9cQc9CNMr76w9XaYYv2pEBHJRfrrLFJA/NRJBC89CZ9/DMXF0K0HZte9oHsPzFbbhj9btYk7TBER2UAq5EQKgC9fjH/iAfzH70LrtpgTzsQccowWohcRyXMq5ESaKO89TPsSP3oU/uP3AMIpQo48CdO8eczRiYhIJqiQE2li/OpV+LFv4d8eBaWzYLMWmP0HYn50MibZOe7wREQkg1TIiTQBPghgyqRwAMNH70BVJWyzPeacIZgBB2E2axF3iCIi0ghUyInkKV9bCzOn4D/5ICzeKsqgpDlm74PDEac9esUdooiINDIVciJ5wgcBzJ2J//Iz/JefwZSJ8O03UFQEO+2OOeVcTP+9Mc03iztUERHJEhVyIjnO19biP3oH/9ITMH9O2Ni5G2bAweEKC3121ehTEZECFWshZ63dChgOdAY8MNQ5d4e19vfAz4HF0aHXOedeiidKkezzy5fC11/gv/oC//nH4bqm3bbBnPsrTN/dMB2ScYcoIiI5IO4euRrgCufcJ9baNsA4a+1r0b7bnXO3xhibSFb4mmqYNwc/e1p4z9vXE9f0vDXfDHr1JXHaz6DfAEwiEW+wIiKSU2It5Jxz84H50fMV1trJQLc4YxJpbH7xAvzE8TB7WrhEVulMqKkJd27WArbvg9n3MEzvnbU8loiIrJPx3scdAwDW2h7AaGBn4HJgELAc+Jiw166intcMBgYDOOf2qKqqyla49SouLqYm9T/kAqY8fD8H3ntqZ0/n27GjqfzgLWpmTgHAtG5Ds569Ke7Zm2Y9d6B4ux0p2rJbk+p103dBOQDlIEV5UA5SiouLSYR/682mnisnCjlrbWvgbeBm59xT1trOQBnhfXM3AV2cc+ev5zR+3rx5jRzpuiWTScrKymKNIRcoD9ChOEHZe2/C5An4yZ9CeRkYA9vtiNl9P0y/vaBTF4zZ5N/hnKbvgnIAykGK8qAcpCSTSUpKSiADhVzs12ystc2AkcAjzrmnAJxzC9P23wu8EFN4Ig3iZ08jePQeFk/7Mmxo2Rr67Ir5scXsOgDTrkO8AYqISJMS96hVAwwDJjvnbktr7xLdPwdwEvBFHPGJrI0PAli+FJYswpcvDkeVzp+LH/MmtG5L67MuZHWPHWDrnphEUdzhiohIExV3j9z+wDnA59baCVHbdcAZ1tr+hJdWZwIXxhOeFDIfBLCwFD9zKixeAOWL8Euioq2ibM0AhZSWrcJBCj85j1bbbMs3unwgIiKNLO5Rq+9S//VhzRknWeeXVcC0L/Ezv8bPmAIzp4QrJ6Rs3gE6dsL06AW77wcdt8B07AQdt4AOnTAtWsYWu4iIFKa4e+REYuWrq/Djx+Dfex0mTwDvoagYuvfA7HMobNsL06NXODChWbO4wxUREfkeFXJSULz34eXSydF6pZM/hW9WhT1qP7aYnfcI72trVhJ3qCIiIuulQk4Kgv/2G/zIB/ETPoSlS8LGDp0wu++L2ftg6L1Lk5q/TURECoMKOWny/LIKgjv/BLOnYXbfL5wOZMd+0GnLJj+Pm4iING0q5KRJ8xPGEgy/Eyq/IfHL6zD9BsQdkoiISMaokJMmyXuPf+4x/AsjYKttSfzsCkzXreMOS0REJKNUyEmT4asqYepk/ORP8RM/gTkzMPsPxJz1S404FRGRJkmFnOQtX1sLs6aGhduXn8HUyVBTDUVFsG1vzFkXYQ4+WvfBiYhIk6VCTvKKr6nGv/8G/vNx8NXn4dQhAN23xRx6DKZPf+jVF7NZi3gDFRERyQIVcpLzvPewagV8PZFg5EOwaF64qsKe+0Offpjeu2Datos7TBERkaxTISc5wdfUQPliWLwAv3gBlC3AL14Ii+dD2UL4ZnV44JbdSFxyI2aXPeINWEREJAeokJNG572HlcuhvAzKF+PLy6BiMZSX4csXhwvQV5SDD9a8qLgZJDuHc71t3zf8uUVX2Gk3TLG+tiIiIqBCTjLMV1dT9cV4grGj8dO+hCVRoVZd9f0Di5tBhyS0T2J67wIdOoXrmXbqDMktoV0HrbQgIiKyHirkZJP4ZRUwZwZ+znT8V5/DlElUVFWCSYRrlm7dE/rvDR2SmPZJ6NgJ2iehzeYaTSoiIrKJVMjJBvGrV4X3r82bDXNn4OfMgLkzYcWyNQd12QpzwBG0HXAAK7psjWnZOrZ4RURECoEKOQGi+9iWlcOiaLDB4vlrBh4sng8rV6w5uLgZdN0as+ue4bQf3XtA9x6Y1m0B2CyZZGVZWTwfREREpICokBP8tC8J7r0Vlixa02gS4T1sW3QJF5rvtCWmUxfo0h06d8MUFcUXsIiIiAAq5AqWX7EsXA1h4nj8mDfDQQdnXhgWa1tsCR220OhQERGRHKf/UxcIX14GM77GT5uMn/wZzJ0R7mjRCrP3IRh7AaaV7mkTERHJJyrkmhDvfbhk1bIKqFiCnz0NP+NrmP41LF0SHlTcDLbvgznpHEyffrDNdpiELpOKiIjkIxVyecAHtbB8WVigLSsPp/yIHn559HxpOSxf+sP52jptiem9c7iIfM8dwsEJzZrF80FEREQko1TI5QDvPaxYCosX4ssWhktSLVkUPl+8IJxQt7b2hy9s1Qbatgsnz+3VF9q2h83Dh9m8PXTrgWnTNvsfSERERLIipws5a+1RwB1AEXCfc+6WmEPaaP7b1WGBVrYwXEO0LK1oK1sIVZXff0HbdpDsjOnZG5IHhYMRNl9TqNG2vXrWREREClzOFnLW2iLgLuAIYC7wkbX2OefcpHgj+6HUWqLVy5bgZ0wJBxaUl4W9aosXwJKF35+HDWCzFuFaolt0wfTdLSzakp3DtuQWmOabxfNhREREJG/kbCEHDACmOuemA1hrRwAnALEUcr66Cr78bE2RVpFa/L0MKpZAdRXl6S8oKg6Xo0puidlm+6hA6xytJdoZWrXRElUiIiKySXK5kOsGzEnbngvsnX6AtXYwMBjAOUcymWy0YIJVK1j8zz+GG4kiEh2SFCe3ILFDX4o6dqYouQXNOneB9kkSyc4k2rYr2EXfi4uLG/W/RT5QDkLKg3IAykGK8qAcpBRncJ7WXC7k1ss5NxQYGm36skZcFsp7T+Kav0KHTrB5O0yiiAAIgJromGQySVlZGdQEUF6+jrM1bd/loYApByHlQTkA5SBFeVAOUpLJJCUlJRk5Vy4XcqXAVmnb3aO2WBhjYLsd43p7ERERkR/I5ULuI6CXtXZbwgLudODMeEMSERERyR05exOXc64GuBh4BZgcNrmJ8UYlIiIikjtyuUcO59xLwEtxxyEiIiKSi3K2R05ERERE1k2FnIiIiEieUiEnIiIikqdUyImIiIjkKRVyIiIiInlKhZyIiIhInlIhJyIiIpKnjPc+7hgypcl8EBERESkIZlNP0JR65EzcD2vtuLhjyIWH8qAcKA/KgXKgPCgHG5SHTdaUCjkRERGRgqJCTkRERCRPqZDLrKFxB5AjlAflIEV5UA5AOUhRHpSDlIzloSkNdhAREREpKOqRExEREclTxXEHkOustfcDxwKLnHM7R20dgMeBHsBMwDrnKqy1BrgDOAZYDQxyzn0SveZc4IbotH9yzj2Uzc+xKay1WwHDgc6E07wMdc7dUUh5sNZuBowGmhP+3jzpnLvRWrstMALoCIwDznHOVVlrmxPmbA9gCXCac25mdK5rgQuAWuAS59wr2f48m8paWwR8DJQ6544ttDxYa2cCKwhjr3HO7VlIvw8p1tp2wH3AzoR/G84HvqJA8mCt7U34WVN6Ar8j/M4XRA4ArLWXAT8j/A58DpwHdKGA/iYAWGt/DfyccDTqvc65f2Tj74J65NbvQeCoOm3XAK8753oBr0fbAEcDvaLHYOBu+K7wuxHYGxgA3Gitbd/okWdODXCFc64vsA8wxFrbl8LKQyVwmHOuH9AfOMpauw/wF+B259z2QAXhHyGinxVR++3RcUR5Ox3YifB79e+oKMo3vwYmp20XYh4Odc71d87tGW0X0u9Dyh3AKOfcjkA/wu9EweTBOfdV9B3oT1iYrAaepoByYK3tBlwC7Bl1dhQR/m4X1N8Ea+3OhEXcAMLfhWOttduThe+CCrn1cM6NBsrrNJ8ApCrkh4AT09qHO+e8c24M0M5a2wX4EfCac67cOVcBvMYPi8Oc5Zybn/qXgnNuBeEf624UUB6iz7Iy2mwWPTxwGPBk1F43B6ncPAkMjP4FdgIwwjlX6ZybAUwl/GXNG9ba7sCPCXtiiD5XweWhHgXz+wBgrd0cOAgYBuCcq3LOLaXA8pBmIDDNOTeLwstBMdDCWlsMtATmU3h/E/oAY51zq51zNcDbwMlk4bugQm7jdHbOzY+eLyC85AhhcTMn7bi5Udva2vOOtbYHsBswlgLLg7W2yFo7AVhE+Ms1DVga/dLC9z/Pd5812r+M8BJDXucg8g/gKiCItjtSeHnwwKvW2nHW2sFRW0H9PgDbAouBB6y1462191lrW1F4eUg5HXgsel4wOXDOlQK3ArMJC7hlhJdSC+1vwhfAgdbajtbaloSXTLciC98FFXKbyDnnKZDlway1rYGRwKXOueXp+wohD8652ugSSnfCfynuGHNIWWetTd0vOi7uWGJ2gHNud8LLI0OstQel7yyE3wfCXpjdgbudc7sBq1hz2QgomDxgrS0BjgeeqLuvqecguux3AmFh3xVoRX71JmaEc24y4WXiV4FRwATCe/3Sj2mU74IKuY2zMOoCJfq5KGovJazAU7pHbWtrzxvW2maERdwjzrmnouaCywNAdPnoTWBfwu7w1KCh9M/z3WeN9m9OeGNvvudgf+D46Gb/EYSXT+6gwPIQ9ULgnFtEeE/UAArv92EuMNc5NzbafpKwsCu0PEBY0H/inFsYbRdSDg4HZjjnFjvnqoGnCP9OFNTfBADn3DDn3B7OuYMI7wv8mix8F1TIbZzngHOj5+cCz6a1/9Raa6Ib4ZdFXaqvAEdaa9tH/3o5MmrLC9H9C8OAyc6529J2FUwerLWdohF6WGtbAEcQ3iv4JnBqdFjdHKRycyrwRvSvseeA0621zaORnr2AD7PzKTadc+5a51x351wPwktJbzjnzqKA8mCtbWWtbZN6Tvg9/oIC+n0AcM4tAOZEIzchvEdsEgWWh8gZrLmsCoWVg9nAPtbaltH/K1Lfg4L5m5Bird0i+rk14f1xj5KF74IKufWw1j4GfAD0ttbOtdZeANwCHGGtnUL4r5FbosNfAqYT3qR5L/BLAOdcOXAT8FH0+GPUli/2B84BDrPWTogex1BYeegCvGmt/Yww9teccy8AVwOXW2unEt7nMSw6fhjQMWq/nOiSk3NuIuAI/9CNAoY49//t3VuIVWUYxvF/ikyGKQhW2MGIuii7mIqhIEgiaioSwYvHoLrIyiIshiAi68KUzmRlNxUKBY7Fo5EVYkbWhUUnyoQORJSGJRmFg1qplXXxrV3L0QlkYHTt/fyu1p619jfffMyeeXm/w+v90u8N1UnjcDzwjqSNlH80q22/Tmd9HlpuA/qrz0U38AAdNg5VMH8pJRPV0jFjUGVkVwKfUI4eGUWpWtBJfxNaXpL0BfAapf8DjMDvQio7RERERDRUMnIRERERDZVALiIiIqKhEshFRERENFQCuYiIiIiGSiAXERER0VAJ5CKisSTNk7TkcPcjIuJwyfEjEXHEkrSr9vIYYA//lb252Xb/CPblBuBOSt3D3yj1JGfZ3inpOUqVg3tHqj8REVBq5UVEHJFsj2tdV2XBbrT95kj3Q9I0ymG3l9veIGkiMH2k+xERMVgCuYhoLEnzgdNtXyvpVGATMBtYAIwD7qZkzpYCpwDLbM+tvX82Jct2AqVKwxzb3x3kW/UA79neAP+evv581cYc4Brgb0l9wNu2p0uaDDwFXATsAh63vbjW77Mp2cUrga+B621vrO7fBdwOjAe2ArfaXjfc8YqI9pM1chHRbs6n1GmcBTwB3EMpjTMVUJVdQ9IMYB6lJuIkYD3718us+wDolXSfpAsldbVu2H4W6AcesT2uCuJGUcr0bKRMxV4C9EnqrbU5A1gBTKTUZFwlaUxVu3Qu0GP7WKAX2DzMMYmINpVALiLazULbu22/AfwKvGD7J9s/UIK1c6rnbgEetP2l7T8pU6fdkqYMbtD2ekrAdy6wGvhF0iJJo4foQw8wyfYC23ttf0upp3h17ZmPba+0/QewCDgauICSpesCzpI0xvZm298Ma0Qiom1lajUi2s222vXvB3ndWnc3BXhS0mO1+0dRMmgHTK/aXgOsqbJtF1OyaV8BzxykD1OAyZIGal8bTQkkW7bU2t4n6Xtgsu311RTtfGCqpLXAHba3Dv0jR0SnSiAXEZ1qC3D/oe58tb0PWCfpLco6N4DB2/+3AJtsn/E/TZ3cuqiCw5Mo6+GwvRxYLmk8JVB8GLjuUPoZEZ0hgVxEdKqngYWSPrX9uaQJwGW2Vwx+sFpPNxZYCwxQpk6nAX3VI9uA02pv+RDYWW1aWAzsBc4Extr+qHrmPEkzgVcpGxv2AO9Xa+ROBN4FdlOyiENN4UZEh8sauYjoSLZfpmS6XpS0A/gMuGKIx7cDN1F2l+4AlgGP1rJ5Sylr2gYkrbL9F3AV0E3ZSfszsASYUGvzFcqGjO2UbNvMar1cF/BQ9Z4fgeMou28jIg6QA4EjIkZY/diUw92XiGi2ZOQiIiIiGiqBXERERERDZWo1IiIioqGSkYuIiIhoqARyEREREQ2VQC4iIiKioRLIRURERDRUArmIiIiIhkogFxEREdFQ/wB9gxbTV5voUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1b2c25efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x7f1b60468550>,\n",
       " <matplotlib.figure.Figure at 0x7f1b2c383d90>,\n",
       " <matplotlib.figure.Figure at 0x7f1b2c25efd0>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotting.plot_episode_stats(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
